The present invention relates generally to plant productivity testing, and more particularly to a system and method of combining plant productivity testing data with environmental data and geographic data of testing sites in a computer program comprising at least one database for receiving, storing, analyzing and outputting the data.
Foundation seed companies and private plant breeding companies are in the business of developing seed products from seed producing plants. This is mainly accomplished through foundation seed company testing programs and private plant breeding programs. However, all of the data is separate and different, and they all publish it in their own way. There is currently no method to connect the data.
Historically, the testing of plant productivity and the response of plants to environmental variables has been conducted in replicated research plots, conditions that favor high yields, on farm test plots and third party yield trials. With no environment measure, researchers are hesitant to test in a variety of conditions. Plant population and design of the plots has been controlled, to a degree, but many variables have not been recorded or cannot be easily and accurately collected. Another problem is that much of the data is proprietary and not available for analysis. Proprietary testing systems have also made it difficult to compare differences in plant productivity.
Genetic developers, such as foundation seed companies and private plant breeding programs, have generated volumes of data documenting yield, grain moisture content, lodging and disease response. Traditionally, testing sites have been chosen based on availability of these sites, a desire to geographically separate the tests, a need for conducting tests in a variety of climates, a need for comparing irrigated versus non-irrigated conditions, and upon budgetary limits. Most of all, there has been a propensity to test in environments that will make seed products look good.
Presently, data related to plant productivity are generally available in large books and digital databases or spreadsheets. Each foundation seed company and private breeding program documents its own proprietary data in individually developed formats. There is little commonality in the data except for possibly statistical analysis, yield notation and harvest moisture. There is no accurate method to analyze data between different data sources.
Huge volumes of data are being produced by seed company testing programs and plant breeding programs. More data is being generated about more seed products than ever before, yet no method or process exists to combine and analyze the data. Large books and binders of expensive data from seed companies are piled on dusty shelves. The ability to view combined data generated by different entities does not exist.
With the explosion in the number of seed products, it will only become more difficult to make product choices. Typically, seed product selection rests on recommendations and environmental trial and error. This is at a time when mistakes will be more harmful as growers demand performance from higher priced products.
Therefore, there is a need for a system and method of pulling all of the plant productivity data from foundation seed companies and private plant breeding companies and combining it with relevant environmental and geographic data of the test sites for analysis in a single computer program.